{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## 导入数据"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "import pandas as  pd\r\n",
    "df = pd.read_csv('./data/Wholesale customers data.csv')\r\n",
    "df.head()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Channel  Region  Fresh  Milk  Grocery  Frozen  Detergents_Paper  Delicassen\n",
       "0        2       3  12669  9656     7561     214              2674        1338\n",
       "1        2       3   7057  9810     9568    1762              3293        1776\n",
       "2        2       3   6353  8808     7684    2405              3516        7844\n",
       "3        1       3  13265  1196     4221    6404               507        1788\n",
       "4        2       3  22615  5410     7198    3915              1777        5185"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "df.info()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 8 columns):\n",
      " #   Column            Non-Null Count  Dtype\n",
      "---  ------            --------------  -----\n",
      " 0   Channel           440 non-null    int64\n",
      " 1   Region            440 non-null    int64\n",
      " 2   Fresh             440 non-null    int64\n",
      " 3   Milk              440 non-null    int64\n",
      " 4   Grocery           440 non-null    int64\n",
      " 5   Frozen            440 non-null    int64\n",
      " 6   Detergents_Paper  440 non-null    int64\n",
      " 7   Delicassen        440 non-null    int64\n",
      "dtypes: int64(8)\n",
      "memory usage: 27.6 KB\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "df.describe()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.322727</td>\n",
       "      <td>2.543182</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.468052</td>\n",
       "      <td>0.774272</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Channel      Region          Fresh          Milk       Grocery  \\\n",
       "count  440.000000  440.000000     440.000000    440.000000    440.000000   \n",
       "mean     1.322727    2.543182   12000.297727   5796.265909   7951.277273   \n",
       "std      0.468052    0.774272   12647.328865   7380.377175   9503.162829   \n",
       "min      1.000000    1.000000       3.000000     55.000000      3.000000   \n",
       "25%      1.000000    2.000000    3127.750000   1533.000000   2153.000000   \n",
       "50%      1.000000    3.000000    8504.000000   3627.000000   4755.500000   \n",
       "75%      2.000000    3.000000   16933.750000   7190.250000  10655.750000   \n",
       "max      2.000000    3.000000  112151.000000  73498.000000  92780.000000   \n",
       "\n",
       "             Frozen  Detergents_Paper    Delicassen  \n",
       "count    440.000000        440.000000    440.000000  \n",
       "mean    3071.931818       2881.493182   1524.870455  \n",
       "std     4854.673333       4767.854448   2820.105937  \n",
       "min       25.000000          3.000000      3.000000  \n",
       "25%      742.250000        256.750000    408.250000  \n",
       "50%     1526.000000        816.500000    965.500000  \n",
       "75%     3554.250000       3922.000000   1820.250000  \n",
       "max    60869.000000      40827.000000  47943.000000  "
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据预处理"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "from sklearn.preprocessing import Normalizer\r\n",
    "transformer = Normalizer()\r\n",
    "transformer = transformer.fit(df)\r\n",
    "norms_data = transformer.transform(df,copy=True)\r\n",
    "norms_data"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[1.11821406e-04, 1.67732109e-04, 7.08332695e-01, ...,\n",
       "        1.19648904e-02, 1.49505220e-01, 7.48085205e-02],\n",
       "       [1.25321880e-04, 1.87982820e-04, 4.42198253e-01, ...,\n",
       "        1.10408576e-01, 2.06342475e-01, 1.11285829e-01],\n",
       "       [1.24839188e-04, 1.87258782e-04, 3.96551681e-01, ...,\n",
       "        1.50119124e-01, 2.19467293e-01, 4.89619296e-01],\n",
       "       ...,\n",
       "       [5.01633106e-05, 7.52449659e-05, 3.64461533e-01, ...,\n",
       "        1.09606834e-02, 3.72236846e-01, 4.68274505e-02],\n",
       "       [9.11309417e-05, 2.73392825e-04, 9.37737390e-01, ...,\n",
       "        9.45939175e-02, 1.53099982e-02, 1.93653251e-01],\n",
       "       [2.41225630e-04, 7.23676891e-04, 6.72295832e-01, ...,\n",
       "        1.56796660e-02, 1.15064626e-01, 1.25437328e-02]])"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 适用轮廓系数找到最优K"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "from sklearn.cluster import KMeans\r\n",
    "from sklearn.metrics import silhouette_score\r\n",
    "\r\n",
    "scores = []\r\n",
    "for k in range(2,7):\r\n",
    "    kmeans = KMeans(n_clusters=k)\r\n",
    "    kmeans = kmeans.fit(norms_data)\r\n",
    "    score = silhouette_score(norms_data,kmeans.labels_)\r\n",
    "    scores.append(score)\r\n",
    "\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "%matplotlib inline\r\n",
    "plt.rcParams['font.sans-serif']=['Simhei']\r\n",
    "plt.rcParams['axes.unicode_minus']=False\r\n",
    "plt.rcParams['font.size']=18\r\n",
    "\r\n",
    "plt.plot(range(2,7),scores)\r\n",
    "plt.xlabel('K')\r\n",
    "plt.ylabel('score')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'score')"
      ]
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 建模及可视化分析"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "source": [
    "kmeans = KMeans(n_clusters=3)\r\n",
    "kmeans = kmeans.fit(norms_data)\r\n",
    "cluster_centers = kmeans.cluster_centers_\r\n",
    "cluster_centers_labels = kmeans.predict(kmeans.cluster_centers_)\r\n",
    "cluster_df = pd.DataFrame(cluster_centers,index=cluster_centers_labels,columns=df.columns)\r\n",
    "cluster_df = cluster_df.drop(columns=['Channel','Region'])\r\n",
    "cluster_df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicassen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.575088</td>\n",
       "      <td>0.233245</td>\n",
       "      <td>0.279252</td>\n",
       "      <td>0.625425</td>\n",
       "      <td>0.053748</td>\n",
       "      <td>0.109052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.262553</td>\n",
       "      <td>0.488689</td>\n",
       "      <td>0.677848</td>\n",
       "      <td>0.095956</td>\n",
       "      <td>0.266153</td>\n",
       "      <td>0.100561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.907702</td>\n",
       "      <td>0.171099</td>\n",
       "      <td>0.226181</td>\n",
       "      <td>0.150525</td>\n",
       "      <td>0.049222</td>\n",
       "      <td>0.071186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fresh      Milk   Grocery    Frozen  Detergents_Paper  Delicassen\n",
       "0  0.575088  0.233245  0.279252  0.625425          0.053748    0.109052\n",
       "1  0.262553  0.488689  0.677848  0.095956          0.266153    0.100561\n",
       "2  0.907702  0.171099  0.226181  0.150525          0.049222    0.071186"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "source": [
    "label_serial = pd.Series(kmeans.labels_)\r\n",
    "label_counts = label_serial.value_counts()\r\n",
    "plt.figure(figsize=(9,6))\r\n",
    "patches ,_,_ =plt.pie(label_counts,autopct='%0.1f%%')\r\n",
    "_ = plt.legend(patches,label_counts.index,loc='upper right',bbox_to_anchor = (0.75,0,0.5,1))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "source": [
    "cluster_df.plot.bar(rot=0,legend=True,figsize=(9,6))"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 60
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.8.11",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.11 64-bit ('ml': conda)"
  },
  "interpreter": {
   "hash": "747b44f12e4aca58ed843b3d1adc2185340143ea37ceaca70a8a99cbf8b68d0c"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}